An ontology is a set of definitions of content-specific knowledge representation primitives: classes, relations, functions, and object constants. Ontolingua is mechanism for writing ontologies in a canonical format, such that they can be easily translated into a variety of representation and reasoning systems. This allows one to maintain the ontology in a single, machine-readable form while using it in systems with different syntax and reasoning capabilities. The syntax and semantics are based on the KIF knowledge interchange format [11]. Ontolingua extends KIF with standard primitives for defining classes and relations, and organizing knowledge in object-centered hierarchies with inheritance. The Ontolingua software provides an architecture for translating from KIF-level sentences into forms that can be efficiently stored and reasoned about by target representation systems. Currently, there are translators into LOOM, Epikit, and Algernon, as well as a canonical form of KIF. This paper describes the asic approach of Ontologia to the ontology sharing problem, introduces the syntax, and describes the semantics of a few ontological commitments made in the software. Those commitments, that are reflected in the ontological syntax and the primitive vocabulary of the frame ontology, include: a distinction between definitional and nondefinitional assertions; the organization of knowledge with classes, instances, sets, and second-order relations; and assertions whose meaning depends on the contents of the knowledge base. Limitations of Ontologia's "conservative" approach to sharing ontologies and alternative approaches to the problem are discussed.

In this paper we present a new semantics, called Local Models Semantics, and use it to provide a foundation to reasoning with contexts. This semantics captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context (of reasoning); however (ii) there is compatibility among the reasoning performed in different contexts. We validate our semantics by formalizing two important forms of contextual reasoning: reasoning with viewpoints and reasoning about belief.

Inamultidatabase system, schematic con icts between two objects are usually of interest only when the objects have some semantic similarity. We use the concept of semantic proximity, which is essentially an abstraction/mapping between the domains of the two objects associated with the context of comparison. An explicit though partial context representation is proposed and the speci city relationship between contexts is de ned. The contexts are organized as a meet semi-lattice and associated operations like the greatest lower bound (glb) are de ned. The context of comparison and the type of abstractions used to relate the two objects form the basis of a semantic taxonomy. Atthesemantic level, the intensional description of database objects provided by the context is expressed in a description logic language. Schema correspondences are used to store mappings from the semantic level to the data level and are associated with the respective contexts. Inferences about database content at the federation level are modeled as changes in the context and the associated schema correspondences. We try to reconcile the dual (schematic and semantic) perspecitves by: enumerating possible semantic similarities between objects having schema and data conicts, and modeling schema correspondences as the projection of semantic proximity wrt context. 1

The Semantic Web consists of many RDF graphs nameable by URIs. This paper

this article was going through many versions as the ideas developed, and the mutual influences cannot be specified. This work was partly supported by DARPA contract NAG2-703 and ARPA/ONR grant N00014-94-1-0775

In this paper we investigate the simple logical properties of contexts. We describe both the syntax and semantics of a general propositional language of context, and give a Hilbert style proof system for this language. A propositional logic of context extends classical propositional logic in two ways. Firstly, a new modality, ist(; OE), is introduced. It is used to express that the sentence, OE, holds in the context . Secondly, each context has its own vocabulary, i.e. a set of propositional atoms which are defined or meaningful in that context. The main results of this paper are the soundness and completeness of this Hilbert style proof system. We also provide soundness and completeness results (i.e. correspondence theory) for various extensions of the general system.

Abstract. The Semantic Web is a powerful vision that is getting to grips with the challenge of providing more human-oriented web services. Hence, reasoning with and across distributed, partially implicit assumptions (contextual knowledge), is a milestone. Ontologies are a primary means to deploy the Semantic Web vision, but few work has been done on them to manage the context-dependency of Web knowledge. In this paper we introduce an ontology for representing a variety of reified contexts and states of affairs, called D&S, currently implemented as a plug-in to the DOLCE foundational ontology, and its application to two cases: an ontology for communication situations and roles, and an ontology for peer-to-peer communication. The reified contexts represented in D&S have a rich structure, and are a middleware between full-fledged formal contexts and theories, and the often poor vocabularies implemented in Web ontologies... 1

It is widely agreed on that most cognitive processes are contextual in the sense that they depend on the environment, or context, inside which they are carried on. Even concentrating on the issue of contextuality in reasoning, many different notions of context can be found in the Artificial Intelligence literature. Our intuition is that reasoning is usually performed on a subset of the global knowledge base. The notion of context is used as a means of formalizing this idea of localization. Roughly speaking, we take a context to be the set of facts used locally to prove a given goal plus the inference routines used to reason about them (which in general are different for different sets of facts). Our perspective is similar to that proposed in [McC87, McC91]. The goal of this paper is to propose an epistemologically adequate theory of reasoning with contexts. The emphasis is on motivations and intuitions, rather than on technicalities. The two basic definitions are reported i...

I wish honorable gentlemen would have the fairness to give the entire context of what I did say, and not pick out detached words. (Cobden, Speeches 46, 1849, quoted intheOED) The importance of contextual reasoning is emphasized by various researchers in AI.

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